In noisy environments, many devices could benefit from the ability to separate a signal of interest from background sounds and noises. For example, in a car when speaking on a cell phone, it would be desirable to separate the voice signal from the road and car noise. Additionally, many voice recognition systems could enhance their performance if such a method was available as a preprocessing filter. Such a capability would also have applications for multi-user detection in wireless communication.
Traditional blind source separation denoising techniques require knowledge or accurate estimation of the mixing parameters of the signal of interest and the background noise. Many standard techniques rely strongly on a mixing model which is unrealistic in real-world environments (e.g., anechoic mixing). The performance of these techniques is often limited by the inaccuracy of the model in successfully representing the real-world mixing mismatch.
Another disadvantage of traditional blind source separation denoising techniques is that standard blind source separation algorithms require the same number of mixtures as signals in order to extract a signal of interest.
What is needed is a signal extraction technique that lacks one or more of these disadvantages, preferably being able to extract signals of interest without knowledge or accurate estimation of the mixing parameters and also not require as many mixtures as signals in order to extract a signal of interest.